code: def test_method_explanations(linear_cls_model, random_cls_dataset):
      """
      Test explanations run and give correct shapes for each backend.
      """
      backend, model, loss_fn, target_fn = linear_cls_model
      params = get_flattened_model_parameters(model)
      (X_train, Y_train), (_, _) = random_cls_dataset
  
      explainer = GradientSimilarity(
          predictor=model,
          loss_fn=loss_fn,
          sim_fn="grad_dot",
          precompute_grads=True,
          backend=backend,
      )
      # test stored gradients
      explainer.fit(X_train=X_train, Y_train=Y_train)
      assert explainer.grad_X_train.shape == (
          len(X_train),
          *params.shape,
      )
      result = explainer.explain(X_train[0])
      assert result.data["scores"].shape == (
          1,
          100,
      )
      assert result.data["ordered_indices"].shape == (
          1,
          100,
      )
  
      result = explainer.explain(X_train[0:4])
      assert result.data["scores"].shape == (
          4,
          100,
      )
      assert result.data["ordered_indices"].shape == (
          4,
          100,
      )
  
file_path: alibi/alibi/explainers/tests/test_simiarlity/test_grad_methods_unit.py
imports: import pytest
  from alibi.explainers.tests.test_simiarlity.conftest import get_flattened_model_parameters
  from alibi.explainers.similarity.grad import GradientSimilarity, Task
  from alibi.utils import _get_options_string
label_desc: Use image node_id 1 for calling a global function with example usage: test_method_explanations(linear_cls_model, random_cls_dataset) without return types